A Bio-inspired Learning and Classification Method for Subcellular Localization of a Plasma Membrane Protein

International audienceHigh-content cellular imaging is an emerging technology for studying many biological phenomena. statistical analyses on large populations (more than thousands) of cells are required. Hence classifying cells by experts is a very time-consuming task and poorly reproducible. In order to overcome such limitations, we propose an automatic supervised classification method. Our new cell classification method consists of two steps: The first one is an indexing process based on specific bio-inspired features using contrast information distributions on cell sub-regions. The second is a supervised learning process to select prototypical samples (that best represent the cells categories) which are used in a leveraged k-NN framework to predict the class of unlabeled cells. In this paper we have tested our new learning algorithm on cellular images acquired for the analysis of changes in the subcellular localization of a membrane protein (the sodium iodide symporter). In order to evaluate the automatic classification performances, we tested our algorithm on a significantly large database of cellular images annotated by experts of our group. Results in term of Mean Avarage Precision (MAP) are very promising, providing precision upper than 87% on average, thus suggesting our method as a valuable decision-support tool in such cellular imaging applications. Such supervised classification method has many other applications in cell imaging in the areas of research in basic biology and medicine but also in clinical histology

The Galapagos Chip Platform for High-Throughput Screening of Cell Adhesive Chemical Micropatterns

In vivo cells reside in a complex extracellular matrix (ECM) that presents spatially distributed biochemical and ‑physical cues at the nano- to micrometer scales. Chemical micropatterning is successfully used to generate adhesive islands to control where and how cells attach and restore cues of the ECM in vitro. Although chemical micropatterning has become a powerful tool to study cell–material interactions, only a fraction of the possible micropattern designs was covered so far, leaving many other possible designs still unexplored. Here, a high-throughput screening platform called “Galapagos chip” is developed. It contains a library of 2176 distinct subcellular chemical patterns created using mathematical algorithms and a straightforward UV-induced two-step surface modification. This approach enables the immobilization of ligands in geometrically defined regions onto cell culture substrates. To validate the system, binary RGD/polyethylene glycol patterns are prepared on which human mesenchymal stem cells are cultured, and the authors observe how different patterns affect cell and organelle morphology. As proof of concept, the cells are stained for the mechanosensitive YAP protein, and, using a machine-learning algorithm, it is demonstrated that cell shape and YAP nuclear translocation correlate. It is concluded that the Galapagos chip is a versatile platform to screen geometrical aspects of cell–ECM interaction

Prediction of eukaryotic protein subcellular multi- localisation with a combined KNN-SVM ensemble classifier

Proteins may exist in or shift among two or more different subcellular locations, and this phenomenon is closely related to biological function. It is challenging to deal with multiple locations during eukaryotic protein subcellular localisation prediction with routine methods; therefore, a reliable and automatic ensemble classifier for protein subcellular localisation is needed. We propose a new ensemble classifier combined with the KNN (K-nearest neighbour) and SVM (support vector machine) algorithms to predict the subcellular localisation of eukaryotic proteins from the GO (gene ontology) annotations. This method was developed by fusing basic individual classifiers through a voting system. The overall prediction accuracies thus obtained via the jackknife test and resubstitution test were 70.5 and 77.6% for eukaryotic proteins respectively, which are significantly higher than other methods presented in the previous studies and reveal that our strategy better predicts eukaryotic protein subcellular localisation

Automatic discovery of cross-family sequence features associated with protein function

BACKGROUND: Methods for predicting protein function directly from amino acid sequences are useful tools in the study of uncharacterised protein families and in comparative genomics. Until now, this problem has been approached using machine learning techniques that attempt to predict membership, or otherwise, to predefined functional categories or subcellular locations. A potential drawback of this approach is that the human-designated functional classes may not accurately reflect the underlying biology, and consequently important sequence-to-function relationships may be missed. RESULTS: We show that a self-supervised data mining approach is able to find relationships between sequence features and functional annotations. No preconceived ideas about functional categories are required, and the training data is simply a set of protein sequences and their UniProt/Swiss-Prot annotations. The main technical aspect of the approach is the co-evolution of amino acid-based regular expressions and keyword-based logical expressions with genetic programming. Our experiments on a strictly non-redundant set of eukaryotic proteins reveal that the strongest and most easily detected sequence-to-function relationships are concerned with targeting to various cellular compartments, which is an area already well studied both experimentally and computationally. Of more interest are a number of broad functional roles which can also be correlated with sequence features. These include inhibition, biosynthesis, transcription and defence against bacteria. Despite substantial overlaps between these functions and their corresponding cellular compartments, we find clear differences in the sequence motifs used to predict some of these functions. For example, the presence of polyglutamine repeats appears to be linked more strongly to the "transcription" function than to the general "nuclear" function/location. CONCLUSION: We have developed a novel and useful approach for knowledge discovery in annotated sequence data. The technique is able to identify functionally important sequence features and does not require expert knowledge. By viewing protein function from a sequence perspective, the approach is also suitable for discovering unexpected links between biological processes, such as the recently discovered role of ubiquitination in transcription

An Empirical Study of Different Approaches for Protein Classification

Many domains would benefit from reliable and efficient systems for automatic protein classification. An area of particular interest in recent studies on automatic protein classification is the exploration of new methods for extracting features from a protein that work well for specific problems. These methods, however, are not generalizable and have proven useful in only a few domains. Our goal is to evaluate several feature extraction approaches for representing proteins by testing them across multiple datasets. Different types of protein representations are evaluated: those starting from the position specific scoring matrix of the proteins (PSSM), those derived from the amino-acid sequence, two matrix representations, and features taken from the 3D tertiary structure of the protein. We also test new variants of proteins descriptors. We develop our system experimentally by comparing and combining different descriptors taken from the protein representations. Each descriptor is used to train a separate support vector machine (SVM), and the results are combined by sum rule. Some stand-alone descriptors work well on some datasets but not on others. Through fusion, the different descriptors provide a performance that works well across all tested datasets, in some cases performing better than the state-of-the-art

The insulin-degrading enzyme: from molecular evolution and subcellular localization to new roles in microglial physiology

La enfermedad de Alzheimer y la diabetes mellitus son dos patologías crónicas con un alarmante incremento en su incidencia y prevalencia a nivel mundial. Se ha propuesto el término "diabetes tipo 3" para describir la hipótesis de que el Alzheimer está causado por un tipo de resistencia a insulina que ocurre específicamente en el cerebro. La enzima degradadora de insulina (IDE) es una metaloproteasa altamente expresada en el cerebro, capaz de degradar in vitro no solo la insulina sino también los péptidos beta amiloides, lo cual convierte a esta proteína een una buena diana para estudiar la diabetes tipo 3. Los resultados presentados en esta Tesis revelan nuevas propiedades biológicas y funciones fisiológicas de IDE en el sistema nervioso, particularmente en la microglía, en la que modula su respuesta multidimensional a diferentes condiciones relevantes en la patogénesis del Alzheimer y la diabetes.Departamento de Bioquímica y Biología Molecular y FisiologíaDoctorado en Investigación Biomédic